Abstract
The research objective of this work is to develop and evaluate an enterprise architecture for rural accommodation in the Iberian Peninsula that responds to the demand of the remote labor market. Through an extensive literature review and the application of ArchiMate modeling, this study focuses on providing an enterprise architecture that promotes business resilience and environmental sustainability and boosts the local economy. The proposed enterprise architecture is remotely evaluated by experts, highlighting potential benefits, challenges, and areas for improvement. The results show that the proposed enterprise architecture has the potential to improve the long-term success of rural lodging businesses, enhance the customer experience, promote sustainability, and contribute to economic growth in rural areas through value exchange among stakeholders. The ArchiMate model provides a holistic perspective on stakeholder interactions and interoperability across all functional business areas: Customer Service, Product Management, Omnichannel Commerce, Human Resources, Business Strategy, Marketing, and Sustainability Management. The idea is to empower rural lodging businesses to create a better customer experience, achieve energy and environmental efficiency, contribute to local development, respond quickly to regulatory changes and compliance, and develop new revenue streams. The main goal is to improve offers, mitigate seasonal effects, and reverse the continuous cycle of decline in areas with low population density. Therefore, this ArchiMate modeling can be the initial basis for the digitization or expansion of the rural lodging industry in other geographies.

Abstract
The adoption of open innovation poses significant challenges that are important to explore. Studies in this field have mainly focused on exploring the causes of the failure of open innovation among large companies. This study addresses this research gap by employing a sample of 297 Portuguese small- and medium-sized enterprises (SMEs) to explore, through a quantitative study, whether the dimensions and causes of failure differ between large organizations and SMEs. A total of seven dimensions of causes of failure are considered, including strategy-related, organizational structure, organizational culture, knowledge and intellectual property management, management skill and action, resources, and interfirm collaboration. The findings reveal significant differences in four of these seven dimensions: the main causes of failure are related to the resources and management processes of open innovation in SMEs, while large companies face more challenges in the organizational structure and culture components. This study offers theoretical insights into the gaps in the literature to better understand the challenges facing open innovation. Furthermore, this study offers practical guidelines for SMEs to identify and mitigate these main obstacles, promoting better innovation management practices.

Abstract
Given the high prevalence of lung cancer, an accurate diagnosis is crucial. In the diagnosis process, radiologists play an important role by examining numerous radiology exams to identify different types of nodules. To aid the clinicians' analytical efforts, computer-aided diagnosis can streamline the process of identifying pulmonary nodules. For this purpose, medical reports can serve as valuable sources for automatically retrieving image annotations. Our study focused on converting medical reports into nodule annotations, matching textual information with manually annotated data from the Lung Nodule Database (LNDb)-a comprehensive repository of lung scans and nodule annotations. As a result of this study, we have released a tabular data file containing information from 292 medical reports in the LNDb, along with files detailing nodule characteristics and corresponding matches to the manually annotated data. The objective is to enable further research studies in lung cancer by bridging the gap between existing reports and additional manual annotations that may be collected, thereby fostering discussions about the advantages and disadvantages between these two data types.

Abstract
Effective traffic management is crucial in addressing the growing complexities of urban mobility, and variable message signs (VMSs) play a vital role in delivering real-time information to road users. Despite their widespread application, there is limited comprehensive understanding of how VMS influence user behavior and optimize traffic flow. This systematic literature review aims to address this gap by examining the effectiveness of VMS in shaping user interactions and enhancing traffic management systems. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, a thorough analysis of relevant studies was conducted to identify key factors influencing VMS impact, including message content and characteristics, complementary sources of information, user demographics, VMS location, and users' reliance on these signs. Additionally, the review explores the implications of displaying non-critical information on VMS and introduces virtual dynamic message signs (VDMSs) as an innovative approach for delivering public traveler information. The study identifies several research gaps, such as the integration of VMS with vehicle-to-everything (V2X) technologies, navigation systems, the need for validation in real-world scenarios, and understanding behavioral responses to non-critical information on VMS. This review highlights the importance of optimizing VMS for improved user engagement and traffic management, providing valuable insights and directions for future research in this evolving field.

Abstract
This paper addresses the critical challenge of optimising beacon placement to support indoor location services and proposes a methodology to optimise the Base Station (BS) coverage keeping or even improving the system precision. The algorithm builds on top of the building schematics and takes into account several aspects that affect the radio link range (materials attenuation, Line of Sight (LOS) conditions, transmitted power and radio sensibility). The outcome is available as a coverage heat map. It is then compared with a standard layout following existing expert guidelines to evaluate the efficacy of the proposed layout.

Abstract
With the growing interest in underwater exploration and monitoring, autonomous underwater vehicles have become essential. The recent interest in onboard deep learning (DL) has advanced real-time environmental interaction capabilities relying on efficient and accurate vision-based DL models. However, the predominant use of sonar in underwater environments, characterized by limited training data and inherent noise, poses challenges to model robustness. This autonomy improvement raises safety concerns for deploying such models during underwater operations, potentially leading to hazardous situations. This article aims to provide the first comprehensive overview of sonar-based DL under the scope of robustness. It studies sonar-based DL perception task models, such as classification, object detection, segmentation, and simultaneous localization and mapping. Furthermore, this article systematizes sonar-based state-of-the-art data sets, simulators, and robustness methods, such as neural network verification, out-of-distribution, and adversarial attacks. This article highlights the lack of robustness in sonar-based DL research and suggests future research pathways, notably establishing a baseline sonar-based data set and bridging the simulation-to-reality gap.